run_svm_mc: SVM multicore tuning.

Description

run_svm_mc is called from within run_svm. It tunes using multiple cores.

Usage

run_svm_mc(
  y,
  L1.x,
  L2.x,
  L2.eval.unit,
  L2.unit,
  L2.reg,
  form,
  loss.unit,
  loss.fun,
  data,
  cores,
  svm.grid,
  verbose
)

Value

The cross-validation errors for all models. A list.

Arguments

y: Outcome variable. A character vector containing the column names of the outcome variable. A character scalar containing the column name of the outcome variable in survey.
L1.x: Individual-level covariates. A character vector containing the column names of the individual-level variables in survey and census used to predict outcome y. Note that geographic unit is specified in argument L2.unit.
L2.x: Context-level covariates. A character vector containing the column names of the context-level variables in survey and census used to predict outcome y. To exclude context-level variables, set L2.x = NULL.
L2.eval.unit: Geographic unit for the loss function. A character scalar containing the column name of the geographic unit in survey and census.
L2.unit: Geographic unit. A character scalar containing the column name of the geographic unit in survey and census at which outcomes should be aggregated.
L2.reg: Geographic region. A character scalar containing the column name of the geographic region in survey and census by which geographic units are grouped (L2.unit must be nested within L2.reg). Default is NULL.
form: The model formula. A formula object.
loss.unit: Loss function unit. A character-valued scalar indicating whether performance loss should be evaluated at the level of individual respondents (individuals), geographic units (L2 units) or at both levels. Default is c("individuals", "L2 units"). With multiple loss units, parameters are ranked for each loss unit and the loss unit with the lowest rank sum is chosen. Ties are broken according to the order in the search grid.
loss.fun: Loss function. A character-valued scalar indicating whether prediction loss should be measured by the mean squared error (MSE) or the mean absolute error (MAE). Default is MSE.
data: Data for cross-validation. A list of \(k\) data.frames, one for each fold to be used in \(k\)-fold cross-validation.
cores: The number of cores to be used. An integer indicating the number of processor cores used for parallel computing. Default is 1.
svm.grid: The hyper-parameter search grid. A matrix of all hyper-parameter combinations.
verbose: Verbose output. A logical argument indicating whether or not verbose output should be printed. Default is FALSE.